T.M. McGinnity

University of Ulster, Aontroim, N Ireland, United Kingdom

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Publications (199)125.01 Total impact

  • Min Jing, T Martin McGinnity, Sonya Coleman, Armin Fuchs, J Kelso
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    ABSTRACT: Despite the emerging applications of diffusion tensor imaging (DTI) to mild traumatic brain injury (mTBI), very few investigations have been reported related to temporal changes in quantitative diffusion patterns, which may help to assess recovery from head injury and the long term impact associated with cognitive and behavioural impairments caused by mTBI. Most existing methods are focused on detection of mTBI affected regions rather than quantification of temporal changes following head injury. Furthermore, most methods rely on large data samples as required for statistical analysis and thus are less suitable for individual case studies. In this work, we introduce an approach based on group spatial independent component analysis (GICA), in which the diffusion scalar maps from an individual mTBI subject and the average of a group of controls are arranged according to their data collection time points. In addition, we propose a constrained GICA (CGICA) model by introducing the prior information into the GICA decomposition process thus taking available knowledge of mTBI into account. The proposed method is evaluated based on DTI data collected from American football players including eight controls and three mTBI subjects (at three time points post injury). The results show that common spatial patterns within the diffusion maps were extracted as spatially independent components (ICs) by GICA. The temporal change of diffusion patterns during recovery is revealed by the time course of the selected IC. The results also demonstrate that the temporal change can be further influenced by incorporating the prior knowledge of mTBI (if available) based on the proposed CGICA model. Although a small sample of mTBI subjects is studied, as a proof of concept, the preliminary results provide promising insight for applications of DTI to study recovery from mTBI and may have potential for individual case studies in practice.
    IEEE Journal of Biomedical and Health Informatics 08/2014; · 1.98 Impact Factor
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    ABSTRACT: It has been found that dysregulation in the orexin/hypocretin (Ox/HCRT) neuropeptide system in the lateral hypothalamus (LHA) is known to affect sleep disorder, depression and motor activities. However, to date there is no common agreement regarding the resulting specific changes induced in the Ox system. In this study, we inject corticosterone to produce stress-induced depressed mice and investigate the Ox neuronal and corresponding behavioural changes. Different doses (10, 20, 50mg/kgbw) of corticosterone were injected in adult mice, and then were tested in the open field test, forced swim test, tail suspension test, elevated plus maze test and motor activity measurements to validate the depressed animal model. Significant dose-dependent behavioural changes were observed in correlation with the doses of corticosterone. The effect is most significant and robust in the high 50mg/kgbw dose group five weeks after injection. Interestingly, we found on average a reduction in motor activity during the 12-hour dark phase (awake) of the depressed mice and no significant change during the light phase (asleep). Finally, using confocal microscopy, immunofluorescence (IF) analysis shows a significant increase (∼20%) in the number of Ox neurons in the LHA of the depressed mice as compared to the age-matched controls. This study suggests that an increase in Ox neuronal signaling may be functionally linked to high and prolonged external stress-induced depression.
    Behavioural brain research. 05/2014;
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    ABSTRACT: Orexinergic/hypocretinergic (Ox) neurotransmission plays an important role in regulating sleep, as well as in anxiety and depression, for which the serotonergic (5-HT) system is also involved in. However, little is known regarding the direct and indirect interactions between 5-HT in the dorsal raphe nucleus (DRN) and Ox neurons in the lateral hypothalamus (LHA). In this study, we report the additional presence of 5-HT1BR, 5-HT2AR, 5-HT2CR and fast ligand-gated 5-HT3AR subtypes on the Ox neurons of transgenic Ox-enhanced green fluorescent protein (Ox-EGFP) and wild type C57Bl/6 mice using single and double immunofluorescence (IF) staining, respectively, and quantify the colocalization for each 5-HT receptor subtype. We further reveal the presence of 5-HT3AR and 5-HT1AR on GABAergic neurons in LHA. We also identify NMDAR1, OX1R and OX2R on Ox neurons, but none on adjacent GABAergic neurons. This suggests a one-way relationship between LHA's GABAergic and Ox neurons, wherein GABAergic neurons exerts an inhibitory effect on Ox neurons under partial DRN's 5-HT control. We also show that Ox axonal projections receive glutamatergic (PSD-95 immunopositive) and GABAergic (Gephyrin immunopositive) inputs in the DRN. We consider these and other available findings into our computational model to explore possible effects of neural circuit connection types and timescales on the DRN-LHA system's dynamics. We find that if the connections from 5-HT to LHA's GABAergic neurons are weakly excitatory or inhibitory, the network exhibits slow oscillations; not observed when the connection is strongly excitatory. Furthermore, if Ox directly excites 5-HT neurons at a fast timescale, phasic Ox activation can lead to an increase in 5-HT activity; no significant effect with slower timescale. Overall, our experimental and computational approaches provide insights towards a more complete understanding of the complex relationship between 5-HT in the DRN and Ox in the LHA.
    PLoS ONE 02/2014; 9(2):e88003. · 3.73 Impact Factor
  • Jinling Wang, Ammar Belatreche, Liam Maguire, Thomas Martin McGinnity
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    ABSTRACT: A novel online learning algorithm for Spiking Neural Networks (SNNs) with dynamically adaptive structure is presented. The main contribution of this work lies in the fact that the proposed adaptive SNN is able to classify spike-based spatio-temporal inputs after just one presentation of the training set, i.e. in one pass only, and does not require the entire training set to be available at once. Both the structure and weights of the SNN are learned dynamically through a combination of unsupervised and supervised learning paradigms. The proposed feed-forward SNN consists of three layers of spiking neurons: an input layer which temporally encodes real valued features into spike-based spatio-temporal patterns, a hidden layer of dynamically grown and pruned neurons which perform spatio-temporal clustering, and an output layer for classification. An unsupervised spiking-based clustering algorithm is implemented by the hidden layer whose spiking neurons are trained to compute a temporal Radial Basis Function (RBF) where incoming inputs will selectively activate hidden neurons based on how close the inputs are to the preferred inputs of the hidden neurons. The centre of each hidden RBF spiking neuron is represented by its time to first spike. In addition, a growing and pruning strategy is proposed to adjust the structure of the hidden layer ‘on-the-fly’ as inputs are presented to the SNN. Both the weights and the centres of the hidden RBF neurons are learned in an unsupervised way and classification at the output layer is achieved through supervised learning where the learning windows proposed for STDP and anti-STDP are used to adjust the weights of the output neurons afferent connections. Competition at both the hidden and the output layers is achieved through the use of lateral inhibitory connections between the neurons of each layer. The proposed online learning algorithm is validated on several benchmark datasets. The evaluation results demonstrate that SNNs trained with the proposed approach require only one pass through the training set in order to classify the inputs with comparable accuracies to existing SNN-based approaches as well as traditional representative classifiers.
    Neurocomputing 01/2014; 144:526–536. · 1.63 Impact Factor
  • Xingfeng Li, Damien Coyle, Liam Maguire, Thomas Martin McGinnity
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    ABSTRACT: The trust region method which originated from the Levenberg-Marquardt (LM) algorithm for mixed effect model estimation are considered in the context of second level functional magnetic resonance imaging (fMRI) data analysis. We first present the mathematical and optimization details of the method for the mixed effect model analysis, then we compare the proposed methods with the conventional expectation-maximization (EM) algorithm based on a series of datasets (synthetic and real human fMRI datasets). From simulation studies, we found a higher damping factor for the LM algorithm is better than lower damping factor for the fMRI data analysis. More importantly, in most cases, the expectation trust region algorithm is superior to the EM algorithm in terms of accuracy if the random effect variance is large. We also compare these algorithms on real human datasets which comprise repeated measures of fMRI in phased-encoded and random block experiment designs. We observed that the proposed method is faster in computation and robust to Gaussian noise for the fMRI analysis. The advantages and limitations of the suggested methods are discussed.
    Magnetic Resonance Imaging 10/2013; · 2.06 Impact Factor
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    ABSTRACT: Price manipulation refers to the act of using illegal trading behaviour to manually change an equity price with the aim of making profits. With increasing volumes of trading, price manipulation can be extremely damaging to the proper functioning and integrity of capital markets. Effective approaches for analysing and real-time detection of price manipulation are yet to be developed. This paper proposes a novel approach, called Hidden Markov Model with Abnormal States (HMMAS), which models and detects price manipulation activities. Together with the wavelet decomposition for features extraction and Gaussian Mixture Model for Probability Density Function (PDF) construction, the HMMAS model detects price manipulation and identifies the type of the detected manipulation. Evaluation experiments of the model were conducted on six stock tick data from NASDAQ and London Stock Exchange (LSE). The results showed that the proposed HMMAS model can effectively detect price manipulation patterns.
    Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics; 10/2013
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    ABSTRACT: Inspired by the behaviour of biological receptive fields and the human visual system, a network model based on spiking neurons is proposed to detect edges in a visual image. The structure and the properties of the network are detailed in this paper. Simulation results show that the network based on spiking neurons is able to perform edge detection within a time interval of 100 ms. This processing time is consistent with the human visual system. A firing rate map recorded in the simulation is comparable to Sobel and Canny edge graphics. In addition, the network can separate different edges using synapse plasticity, and the network provides an attention mechanism in which edges in an attention area can be enhanced.
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    ABSTRACT: Two-dimensional integrate-and-fire neuronal models, such as the adaptive exponential[1] or quadratic integrate-and-fire[2] (AdEx or Izhikevich) model, compromise between computational efficiency and the ability to generate diverse realistic spiking patterns ([1–3]). However, when searching for model parameters to fit the neuronal spiking patterns observed in electrophysiological recording, it often requires expert knowledge e.g. in dynamical systems theory, or a tedious trial-and-error approach. In this work, we offer a systematic computational approach towards solving this problem. First, for each free parameter, a list of plausible values is defined. Then every possible parameter set is analysed, and the features of the simulated neurons are extracted and saved. The second step is to define the target spiking patterns. The user also needs to define a window size, and a required number of instances for each target pattern. The program then uses a sliding window approach on the feature map built in the first step, counting the instances of each pattern, and provides the list of parameter sets that fit the description. The computer program is written in C++ to generate results efficiently. To test our approach, we focus on the AdEx model parameters to replicate the electrophysiological data of neurons in the lateral habenula (LHb). Experimental data has shown that although LHb neurons can have different morphologies, their basic electrophysiological characterizations are very similar[4]. They also display time-dependent inward rectification and distinct afterhyperpolarization. Furthermore, LHb neurons can exhibit distinctive spontaneous spiking patterns: silent, tonic regular spiking, tonic irregular spiking, and rhythmic bursting. Importantly, rebound bursts can be activated upon brief membrane hyperpolarization. In the model, the essential features to be considered are the adaptation rate, sag, spontaneous spiking frequency, number of spikes per burst, and regularity of the pattern, but other features like spike width, after-hyperpolarization potential could also be extracted. Our toolbox allows us to identify a small region in the parameter space containing the right neuronal proportions for the observed spiking patterns. As a result, we can reproduce the diversity of LHb spiking patterns with small parameter variations within a single model. Although the current implementation considers only two-dimensional integrate-and-fire models, it can be modified or expanded to other types of neuronal models. References [1] R. Brette and W. Gerstner, “Adaptive exponential integrate-and-fire model as an effective description of neuronal activity.,” Journal of neurophysiology, vol. 94, no. 5, pp. 3637-42, Nov. 2005. [2] E. M. Izhikevich, “Simple Model of Spiking Neurons,” IEEE Transactions on Neural Networks, vol. 14, no. 6, pp. 1569-1572, 2003. [3] J. Touboul and R. Brette, “Spiking Dynamics of Bidimensional Integrate-and-Fire Neurons,” SIAM Journal on Applied Dynamical Systems, vol. 8, no. 4, pp. 1462-1506, Jan. 2009. [4] T. Weiss and R. W. Veh, “Morphological and electrophysiological characteristics of neurons within identified subnuclei of the lateral habenula in rat brain slices.,” Neuroscience, vol. 172, pp. 74-93, Jan. 2011.
    Neuroinformatics 2013; 06/2013
  • Pawan Goyal, Laxmidhar Behera, T. M. McGinnity
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    ABSTRACT: In this paper, a novel neighborhood based document smoothing model for information retrieval has been proposed. Lexical association between terms is used to provide a context sensitive indexing weight to the document terms, i.e. the term weights are redistributed based on the lexical association with the context words. A generalized retrieval framework has been presented and it has been shown that the vector space model (VSM), divergence from randomness (DFR), Okapi Best Matching 25 (BM25) and the language model (LM) based retrieval frameworks are special cases of this generalized framework. Being proposed in the generalized retrieval framework, the neighborhood based document smoothing model is applicable to all the indexing models that use the term-document frequency scheme. The proposed smoothing model is as efficient as the baseline retrieval frameworks at runtime. Experiments over the TREC datasets show that the neighborhood based document smoothing model consistently improves the retrieval performance of VSM, DFR, BM25 and LM and the improvements are statistically significant.
    Information Retrieval 06/2013; 16(3). · 0.63 Impact Factor
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    H. He, T. M. McGinnity, S. Coleman, B. Gardiner
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    ABSTRACT: Machine learning enables the creation of a non-linear mapping that describes robot-environment interaction, while computing linguistics make the interaction transparent. In this paper, we develop a novel application of a linguistic decision tree for a robot route learning problem by dynamically deciding the robot's behaviour, which is decomposed into atomic actions in the context of a specified task. We examine the real-time performance of training and control of a Linguistic Decision Tree, and explore the possibility of training a machine learning model in an adaptive system without dual CPUs for parallelisation of training and control. A quantified evaluation approach is proposed, and a score is defined for the evaluation of a model's robustness regarding the quality of training data. Compared with the non-linear system identification NARMAX model structure with offline parameter estimation, the linguistic decision tree model with online LID3 learning achieves much better performance, robustness and reliability.
    IEEE transactions on neural networks and learning systems 04/2013; · 3.77 Impact Factor
  • P. Goyal, L. Behera, T.M. McGinnity
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    ABSTRACT: Existing models for document summarization mostly use the similarity between sentences in the document to extract the most salient sentences. The documents as well as the sentences are indexed using traditional term indexing measures, which do not take the context into consideration. Therefore, the sentence similarity values remain independent of the context. In this paper, we propose a context sensitive document indexing model based on the Bernoulli model of randomness. The Bernoulli model of randomness has been used to find the probability of the cooccurrences of two terms in a large corpus. A new approach using the lexical association between terms to give a context sensitive weight to the document terms has been proposed. The resulting indexing weights are used to compute the sentence similarity matrix. The proposed sentence similarity measure has been used with the baseline graph-based ranking models for sentence extraction. Experiments have been conducted over the benchmark DUC data sets and it has been shown that the proposed Bernoulli-based sentence similarity model provides consistent improvements over the baseline IntraLink and UniformLink methods [1].
    IEEE Transactions on Knowledge and Data Engineering 01/2013; 25(8):1693-1705. · 1.89 Impact Factor
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    ABSTRACT: A context-aware cognitive system is a prime requirement for a sensor rich smart home environment. In this paper, we discuss the development and evaluation of a self-sustaining cognitive architecture for the RUBICON (Robotic UBIquitous COgnitive Network) system which builds its knowledge as per the environmental situations. The proposed cognitive architecture consists of a reasoning module, a decision module, and a supporting memory module. An online sliding-window based self-organising fuzzy neural network (SOFNN), which explores relationships between the event inputs and desired reasoning outputs, is developed for the reasoning module. We also propose a prediction model based on event information to support the reasoning module for continuous training in the absence of external training data. The decision module generates control goals for the robots according to the status outputs from the reasoning module. We develop a MySQL based database for the memory module which supports the overall system by storing processed information about the states of the environment and providing historical information for enhanced understanding. The architecture is trained and tested with environmentally realistic synthesized data to show its adaptation capabilities. The results demonstrate that the proposed system can learn activities and track them within a smart home environment. This initial implementation also highlights the potential of the architecture and will serve as a very important test-bed for future work. We envisage that the proposed combination of the prediction model and the reasoning module will eventually result in a general purpose, self-sustaining, self-organising cognitive architecture for different applications and thus the proposed architecture enters into the sphere of the biologically inspired cognitive architecture (BICA) challenge.
    Biologically Inspired Cognitive Architectures. 01/2013; 6:96–108.
  • D. Kerr, S.A. Coleman, T.M. McGinnity, M. Clogenson
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    ABSTRACT: The recent development of low cost cameras that capture 3-dimensional images has changed the focus of computer vision research from using solely intensity images to the use of range images, or combinations of RGB, intensity and range images. The low cost and widespread availability of the hardware to capture these images has realised many possible applications in areas such as robotics, object recognition, surveillance, manipulation, navigation and interaction. Given the large volumes of data in range images, processing and extracting the relevant information from the images in real time becomes challenging. To achieve this, much research has been conducted in the area of bio-inspired feature extraction which aims to emulate the biological processes used to extract relevant features, reduce redundancy, and process images efficiently. Inspired by the behaviour of biological vision systems, an approach is presented for extracting important features from intensity and range images, using biologically inspired spiking neural networks in order to model aspects of the functional computational capabilities of the visual system.
    Neural Networks (IJCNN), The 2013 International Joint Conference on; 01/2013
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    ABSTRACT: Linear time series models, such as the autoregressive integrated moving average (ARIMA) model, are among the most popular statistical models used to forecast time series. In recent years non-linear computational models, such as artificial neural networks (ANN), have been shown to outperform traditional linear models when dealing with complex data, like financial time series. This paper proposes a novel hybrid forecasting model which exploits the linear modelling strengths of the ARIMA model, and the flexibility of a self-organising fuzzy neural network (SOFNN). The system's performance is evaluated using several datasets, and our results indicate that a hybrid system is an effective tool for time series forecasting.
    Neural Networks (IJCNN), The 2013 International Joint Conference on; 01/2013
  • Xingfeng Li, Damien Coyle, Liam Maguire, Thomas Martin McGinnity
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    ABSTRACT: We present a least trimmed square (LTS) robust regression method to combine different runs/subjects for second/high level effective connectivity analysis. The basic idea of this method is to treat the extreme nonlinear model variability as outliers if they exceed a certain threshold. A bootstrap method for the LTS estimation is employed to detect model outliers. We compared the LTS robust method with a non-robust method using simulated and real datasets. The difference between LTS and the non-robust method for second level effective connectivity analysis is significant, suggesting the conventional non-robust method is easily affected by the model variability from the first level analysis. In addition, after these outliers are detected and excluded for the high level analysis, the model coefficients of the second level are combined within the framework of a mixed model. The variance of the mixed model is estimated using the Newton-Raphson (NR) type Levenberg-Marquardt algorithm. Three sets of real data are adopted to compare conventional methods which do not include random effects in the analysis with a mixed model for second level effective connectivity analysis. The results show that the conventional method is significantly different from the mixed model when greater model variability exists, suggesting there is a strong random effect, and the mixed model should be employed for the second level effective connectivity analysis.
    Neuroinformatics 10/2012; · 3.14 Impact Factor
  • S. P.johnston, G.prasad, L.maguire, T. M.mcginnity
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    ABSTRACT: This paper presents an approach that permits the effective hardware realization of a novel Evolvable Spiking Neural Network (ESNN) paradigm on Field Programmable Gate Arrays (FPGAs). The ESNN possesses a hybrid learning algorithm that consists of a Spike Timing Dependent Plasticity (STDP) mechanism fused with a Genetic Algorithm (GA). The design and implementation direction utilizes the latest advancements in FPGA technology to provide a partitioned hardware/software co-design solution. The approach achieves the maximum FPGA flexibility obtainable for the ESNN paradigm. The algorithm was applied as an embedded intelligent system robotic controller to solve an autonomous navigation and obstacle avoidance problem.
    International Journal of Neural Systems 04/2012; 20(06). · 5.05 Impact Factor
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    ABSTRACT: This paper proposes a supervised training algorithm for Spiking Neural Networks (SNNs) which modifies the Spike Timing Dependent Plasticity (STDP)learning rule to support both local and network level training with multiple synaptic connections and axonal delays. The training algorithm applies the rule to two and three layer SNNs, and is benchmarked using the Iris and Wisconsin Breast Cancer (WBC) data sets. The effectiveness of hidden layer dynamic threshold neurons is also investigated and results are presented.
    International Journal of Neural Systems 04/2012; 20(06). · 5.05 Impact Factor
  • Lorenzo Riano, T.M. McGinnity
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    ABSTRACT: We propose a robotics algorithm that is able to simultaneously combine, adapt and create actions to solve a task. The actions are combined in a Finite State Automaton whose structure is determined by a novel evolutionary algorithm. The actions parameters, or new actions, are evolved alongside the FSA topology. Actions can be combined together in a hierarchical fashion. This approach relies on skills that with which the robot is already provided, like grasping or motion planning. Therefore software reuse is an important advantage of our proposed approach. We conducted several experiments both in simulation and on a real mobile manipulator PR2 robot, where skills of increasing complexity are evolved. Our results show that (i) an FSA generated in simulation can be directly applied to a real robot without modifications and (ii) the evolved FSA is robust to the noise and the uncertainty arising from real-world sensors.
    Robotics and Autonomous Systems. 04/2012; 60(4):639–650.
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    ABSTRACT: Serotonin (5-HT) plays an important role in regulating mood, cognition and behaviour. The midbrain dorsal raphe nucleus (DRN) is one of the primary sources of 5-HT. Recent studies show that DRN neuronal activities can encode rewarding (e.g., appetitive) and unrewarding (e.g., aversive) behaviours. Experiments have also shown that DRN neurons can exhibit heterogeneous spiking behaviours. In this work, we build and study a basic spiking neuronal network model of the DRN constrained by neuronal properties observed in experiments. We use an efficient adaptive quadratic integrate-and-fire neuronal model to capture slow afterhyperpolarization current, occasional bursting behaviours in 5-HT neurons, and fast spiking activities in the non-5-HT inhibitory neurons. Provided that our noisy and heterogeneous spiking neuronal network model adopts a feedforward inhibitory network architecture, it is able to replicate the main features of DRN neuronal activities recorded in monkeys performing a reward-based memory-guided saccade task. The model exhibits theta band oscillation, especially among the non-5-HT inhibitory neurons during the rewarding outcome of a simulated trial, thus forming a model prediction. By varying the inhibitory synaptic strengths and the afferent inputs, we find that the network model can oscillate over a range of relatively low frequencies, allow co-existence of multiple stable frequencies, and spike synchrony can spread from within a local neural subgroup to global. Our model suggests plausible network architecture, provides interesting model predictions that can be experimentally tested, and offers a sufficiently realistic multi-scale model for 5-HT neuromodulation simulations.
    Neural networks: the official journal of the International Neural Network Society 02/2012; 32:15-25. · 1.88 Impact Factor
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    ABSTRACT: In diffusion-weighted imaging (DWI), reliable fiber tracking results rely on the accurate reconstruction of the fiber orientation distribution function (fODF) in each individual voxel. For high angular resolution diffusion imaging (HARDI), deconvolution-based approaches can reconstruct the complex fODF and have advantages in terms of computational efficiency and no need to estimate the number of distinct fiber populations. However, HARDI-based methods usually require relatively high b-values and a large number of gradient directions to produce good results. Such requirements are not always easy to meet in common clinical studies due to limitations in MRI facilities. Moreover, most of these approaches are sensitive to noise. In this study, we propose a new framework to enhance the performance of the spherical deconvolution (SD) approach in low angular resolution DWI by employing a single channel blind source separation (BSS) technique to decompose the fODF initially estimated by SD such that the desired fODF can be extracted from the noisy background. The results based on numerical simulations and two phantom datasets demonstrate that the proposed method achieves better performance than SD in terms of robustness to noise and variation in b-values. In addition, the results show that the proposed method has the potential to be applied to low angular resolution DWI which is commonly used in clinical studies.
    IEEE transactions on bio-medical engineering 02/2012; 59(2):363-73. · 2.15 Impact Factor

Publication Stats

1k Citations
125.01 Total Impact Points

Institutions

  • 1970–2014
    • University of Ulster
      • • Intelligent Systems Research Centre
      • • School of Computing and Intelligent Systems
      • • School of Engineering
      Aontroim, N Ireland, United Kingdom
  • 2008
    • Ulster Teachers University
      Mississippi, United States
  • 2007
    • Queen's University Belfast
      • School of Electronics, Electrical Engineering and Computer Science
      Béal Feirste, N Ireland, United Kingdom
  • 2003
    • University of Ulsan
      Urusan, Ulsan, South Korea